Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices
Radiomics is the established approach for CT-based lung cancer phenotyping, yet comparisons with foundation models rarely isolate contributions of feature extractor, classification head, and segmentation choice, or test cross-cohort robustness. We benchmark five feature extractors (Curia, Curia-2, DINOv3, Radiomics2D, Radiomics3D), seven classification heads (TabPFN, TabICL, XGBoost, CatBoost, Random Forest, logistic regression, Ridge), and three segmentation regimes on five tasks: tumor volume and stage classification, 2-year survival prediction, histology classification, and age prediction.
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- LinkedLinked via arxiv author · 85%Nils Neukirch →
“Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, a”
- LinkedLinked via arxiv author · 85%Martin Maurer →
“Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, a”
- LinkedLinked via arxiv author · 85%Nils Strodthoff →
“Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, a”
- PossiblePossibly related (embedding) · 49%mlmed/torchxrayvision →
- PossiblePossibly related (embedding) · 53%Integrated data and machine learning transform lung cancer diagnosis and treatment - Bioengineer.org →
- PossiblePossibly related (embedding) · 53%Multi-Source Data-Driven Machine Learning Reshapes the Diagnosis and Treatment of Lung Cancer - EIN Presswire →